CN114611800B - Method for predicting medium-long term trend of yellow sea green tide - Google Patents
Method for predicting medium-long term trend of yellow sea green tide Download PDFInfo
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Abstract
The invention discloses a method for predicting medium-long term trend of yellow sea green tide, which comprises the following steps: a, determining a Huang Hailu tide research area (33-37 DEG N,119-123 DEG E), dividing the research area into two key areas for green tide generation and development by taking 35 DEG N as a boundary, and determining main factors affecting the growth and drift of the green tide as meteorological factors and ocean factors; b, acquiring green tide multisource monitoring data and meteorological and ocean element observation data of a key area; c, analyzing early weather influence factors and sea influence factors of three indexes of green tide satellite discovery time, green tide main body drifting direction and green tide maximum distribution area, and respectively establishing a prediction model; d, obtaining a meteorological element value and a marine element value of the early period of the green tide of the year to be predicted, and carrying out medium-long term trend prediction on indexes such as the satellite discovery time, the main body drift direction, the maximum distribution area and the like of the year Huang Hailu tide according to the prediction model established in the step c to obtain a prediction result. The invention can provide necessary technical support for the green tide disaster prevention and reduction work.
Description
Technical Field
The invention relates to the field of green tide disaster early warning, in particular to a method for predicting medium-long-term trend of yellow sea green tide.
Background
Green tide refers to the abnormal phenomenon of marine ecology that marine macroalgae proliferate or aggregate to form large-area floating under certain environmental conditions. The phenomenon that green tides outbreak and cause serious damage in the global coastal sea areas is becoming more frequent. The geographic area in which green tides occur is increasing, and has become a worldwide marine disaster. Since 1980, green tide disasters have occurred in countries such as the united states, canada, the netherlands, france, italy, japan, and korea, which are particularly severe in coastal areas of france. Coastal cities endangered by green tide increased from 60 to 103 from 1997 to 2001. In 2007, the south and middle partial sea areas of yellow sea in China first found green tide caused by the massive proliferation of green algae, followed by a large scale yellow sea green tide disaster for more than 10 consecutive years.
The research of foreign scholars on green tide prediction is mainly focused on the aspects of growth mechanism, drift settlement and the like of green tide, and preliminary results are obtained. Aurauseau established a three-dimensional bio-geochemical model in the Brest bay where the green tide disaster was severe in france, simulating the growth and spoilage-drift-settlement process of the floating green tide in this sea area. Cugier establishes a three-dimensional ecological model and a three-dimensional hydrodynamic model of phytoplankton, establishes a three-dimensional bioelectrochemical model suitable for the characteristics of green tide in Brest bay, and simultaneously develops a forecast study of green tide in intertidal zone. Perrot developed a simple method of predicting the inter-tidal band seaweed and established in 2007 a predictive model of green tide growth and drift in the inter-tidal band. It can be seen that most foreign research on green tide prediction is aimed at bay and intertidal zone areas, the prediction time is short, and yellow sea green tide occurs in open sea, and large-scale and long-time drift is needed in the process of generation and development. These studies are therefore not well applicable to long-term trend predictions in Huang Hailu tides.
The research of domestic scholars on green tide prediction is mainly developed from the aspects of biology and ocean atmospheric environment dynamics, and certain progress is achieved. Zhang Suping and clothes stand and the like respectively conduct research and analysis on green tide aggregation and directional movement in 2008 and 2009 from the aspect of change of hydrographic meteorological conditions of the yellow sea, and the wind field is considered to be a main forced field for green tide drifting; a key factor leading to the late onset of green tides in 2009 is western precipitation in yellow sea rather than sea surface temperature; the annual change of the ocean surface flow field under the driving of the wind field is the main reason of the green tide drift path variation. Huang Juan based on the three-dimensional full-power POM ocean mode and 2008-2009 Huang Hailu tide multisource actual measurement and monitoring data, the Lagrange particle tracking method is utilized to conduct emergency prediction on the green tide drift track, and the obtained yellow sea green tide drift track has close relation with the digital model result. Lin researches green tide causes and power mechanisms from the aspect of interdisciplinary science, discovers that floating enteromorpha has high genetic uniformity in species and intra-species levels, speculates that the occurrence, development and movement processes of stable cold vortex in the middle of south yellow sea in 4-5 months of 2008 are closely related to drifting aggregation of enteromorpha, and can predict the generation, the elimination and the drifting of green tide by using the cold vortex. The above study, in combination with the development of Huang Hailu tide in a specific year, suggests local factors that affect green tide outbreaks and drift, and preliminary analysis of the influencing mechanism was performed. However, current research does not suggest a general approach to seasonal scale trend prediction of yellow sea green tide, nor considers the effects of atmospheric and marine environments on green tide development.
Disclosure of Invention
Based on the technical problems, the invention provides a method for predicting the medium-long term trend of yellow sea green tide.
The technical scheme adopted by the invention is as follows:
a method for predicting medium-long term trend of yellow sea green tide comprises the following steps: a, dividing a yellow sea area into two key areas for green tide generation and development by using 35 DEG N as a boundary, namely an area 1 and an area 2, wherein the area 1 ranges from 33 DEG N to 35 DEG N,119 DEG E to 123 DEG E, the area 2 ranges from 35 DEG N to 37 DEG N,119 DEG E to 123 DEG E, and main factors affecting the green tide growth and drift are determined to be weather factors and ocean factors;
b, acquiring multi-source historical monitoring data of green tide satellite remote sensing, unmanned aerial vehicles, ships and the like, and acquiring a meteorological and marine element historical analysis data set of a key area;
c, analyzing early weather and ocean influence factors of three indexes of green tide satellite discovery time, main body drift direction and maximum distribution area based on the acquired monitoring data, and establishing a prediction model;
c1 satellite finding time prediction model
Constructing a satellite first-time discovery time prediction factor: sea temperature X sst Salinity X of sea water sal And precipitation rate X prate The method comprises the steps of carrying out a first treatment on the surface of the Establishing a ternary regression model, wherein the ternary regression model is shown as the following formula (1);
Y date ＝-2.90X sst -15.17X sal +3.77X prate +496.32 (1)
the selected area and time period of each factor are as follows: x is X sst Zone 1,2 months 5 to 5 months 1 (1 = 5 days, the same applies below); x is X sal Zone 1,3 months 6 to 5 months 4; x is X prate Zone 1,2 months 2 to 4 months 5;
the actual generation time of green tide is 5 months 17+Y date Day of the year. For example: when Y is date When= -1, the actual occurrence time is 5 months and 16 days; when Y is date When=2, the actual occurrence time is 5 months and 19 days. The 5 month 17 day is the green tide history average satellite finding date.
C2 green tide main body drifting direction prediction model
According to the drift direction of the green tide body, classifying the green tide body into a Sijin type, a North partial Sixi type, a North upper type and a North partial east type;
constructing a prediction factor of the drift direction of the green tide body: flow direction X ocn_deg_1 And wind direction X wnd_deg_2 The method comprises the steps of carrying out a first treatment on the surface of the Establishing a binary regression model, wherein the binary regression model is represented by the following formula (2);
Y greentide_deg ＝1.03X ocn_deg_1 +0.80X wnd_deg_2 -82.81 (2)
the selected area and time period of each factor are as follows: x is X ocn_deg_1 1,6 months 1 to 3 days; x is X wnd_deg_2 2,5 months 6 to 6 months 3;
the drift direction angle of the green tide body is Y greentide_deg The method comprises the steps of carrying out a first treatment on the surface of the For example: 90 degrees indicates that the green tide body drifts in the north-north direction, and 135 degrees indicates that the green tide body drifts in the northwest direction;
c3 green tide maximum distribution area prediction model
Constructing a prediction factor of the maximum distribution area of green tide: sea temperature X sst And the weft component X of the flow ocn_u The method comprises the steps of carrying out a first treatment on the surface of the Establishing a binary regression model, wherein the binary regression model is represented by the following formula (3);
S＝6.36×10 3 X sst +3.96×10 5 X ocn_u -1.33×10 5 (3)
the selected area and time period of each factor are as follows: x is X sst Zone 1,3 months 4 to 4 months 6; x is X ocn_u Zone 1,6 months 2 to 5 days.
The maximum distribution area of the green tide is S;
d, obtaining a meteorological element value and a marine element value of the early stage of the green tide of the required prediction year, and predicting the occurrence and development trend of the green tide of the yellow sea according to the prediction model established in the step c to obtain a prediction result.
Preferably, in step b: the meteorological elements comprise air temperature, illumination, precipitation, wind direction and wind speed; the ocean elements include sea temperature, ocean current, wave height and wave direction.
Preferably, in step c: the prediction model is established by adopting methods of synthesis analysis, lead-lag correlation analysis, multiple regression analysis and the like through meteorological element data, ocean element data and green tide monitoring data corresponding to the current year.
Preferably, each of the weather element data and the ocean element data is weather-averaged and area-averaged prior to establishing the prediction model.
The beneficial technical effects of the invention are as follows:
the invention provides a method for predicting medium-long trend of yellow sea green tide, which is used for integrating latest green tide multisource monitoring data and atmospheric ocean analysis data, analyzing the annual change characteristics and occurrence development trend of Huang Hailu tide, further analyzing the influence of regional atmosphere and ocean environment elements on the yellow sea green tide, establishing a yellow sea green tide occurrence and development trend prediction model, developing green tide disaster prevention and reduction work deployment in advance for related departments, and making an emergency treatment scheme to provide necessary technical support.
Drawings
The invention is further described with reference to the drawings and detailed description which follow:
FIG. 1 is a schematic diagram of a research area of the method of the present invention;
FIG. 2 shows satellite first-time discovery time versus atmospheric and ocean element correlation coefficients;
FIG. 3 is a graph showing a satellite first finding time optimal unitary/binary/ternary regression model versus measured values;
fig. 4 shows green tide body drift path classification diagrams of 2008 to 2019;
FIG. 5 is a composite diagram of 5 months 4-6 days of wind fields and flow fields (upwind field; downflow field; four paths corresponding in sequence from left to right);
FIG. 6 is a 6 month 1-3 day wind field and flow field composite diagram (upwind field; downflow field; four paths corresponding in sequence from left to right);
FIG. 7 is a 6-month 4-6-day wind field and flow field composite diagram (upwind field; downflow field; four paths corresponding in sequence from left to right);
FIG. 8 shows the correlation coefficient of the flow velocity of the flow direction of the region 1 and the drift angle;
FIG. 9 shows the correlation coefficient of wind direction wind speed and drift angle for region 2;
FIG. 10 shows a graph of green tide drift direction optimal unary/binary regression model fit versus measured;
fig. 11 is a graph of maximum distribution area and maximum coverage area for each year from 2008 to 2019;
FIG. 12 shows the correlation coefficient of the maximum green tide distribution area with atmospheric and marine elements;
FIG. 13 is a graph showing the comparison of the fitted value of the green tide maximum distribution area optimal unitary/binary regression model with the actual measurement value.
Detailed Description
Huang Hailu tide outbreaks have been over ten years old, and the time-space characteristics of green tide generation time, drift path, login time, distribution area, coverage area and the like are different in each year. Particularly, in recent years, in the global climate change background, the extreme and abnormal phenomena of offshore marine environments in China are increased, so that the marine atmospheric factors influencing the green tide burst and drift trend are complex, and the prediction difficulty is increased.
Therefore, the change characteristics of Huang Hailu tide are recognized, the influence of ocean and atmospheric environmental factors on green tide is analyzed, and the dynamic process of Huang Hailu tide burst and drift is well understood; the method for predicting the medium-long term trend is established, the explosion and drifting paths of the yellow sea green tide are effectively predicted, the green tide disaster prevention and reduction work deployment can be developed in advance for related departments, and a necessary scientific basis is provided for making an emergency treatment scheme.
Huang Hailu tide occurs at a time typically from 4 to 8 months per year. Initially found by the vessel in mid-4; 5 months to reach the satellite visible scale; then continuously growing and drifting to the north on a large scale; the green tide area reaches the peak value from the middle ten days of 6 months to 7 months, and starts to log on the southern coast of the Shandong peninsula; after which the area begins to decay to gradually die before and after 8 months.
The occurrence and development of green tide also have obvious annual differences due to the annual changes of the marine climate environment in the global and yellow sea areas. The method mainly aims at three elements of green tide satellite discovery time, main body drift direction and maximum distribution area to establish a prediction model. The three elements can comprehensively describe the development characteristics of green tide and are key factors for determining the influence of the green tide disaster on society and economy.
The invention provides a method for predicting medium-long term trend of yellow sea green tide, which comprises the following steps:
a, dividing a yellow sea area into two key areas for green tide generation and development by using 35 DEG N as a boundary, namely an area 1 and an area 2, wherein the area 1 ranges from 33 DEG N to 35 DEG N,119 DEG E to 123 DEG E, the area 2 ranges from 35 DEG N to 37 DEG N,119 DEG E to 123 DEG E, and main factors affecting the green tide growth and drift are determined to be weather factors and ocean factors;
b, acquiring multi-source historical monitoring data of green tide satellite remote sensing, unmanned aerial vehicles, ships and the like, and acquiring a meteorological and marine element historical analysis data set of a key area;
c, analyzing early weather and ocean influence factors of three indexes of green tide satellite discovery time, main body drift direction and maximum distribution area based on the acquired monitoring data, and establishing a prediction model;
c1 satellite finding time prediction model
Constructing a satellite first-time discovery time prediction factor: sea temperature X sst Salinity X of sea water sal And precipitation rate X prate The method comprises the steps of carrying out a first treatment on the surface of the Establishing a ternary regression model, wherein the ternary regression model is shown as the following formula (1);
Y date ＝-2.90X sst -15.17X sal +3.77X prate +496.32 (1)
the selected area and time period of each factor are as follows: x is X sst Zone 1,2 months 5 to 5 months 1 (1 = 5 days, the same applies below);X sal zone 1,3 months 6 to 5 months 4; x is X prate Zone 1,2 months 2 to 4 months 5.
The actual satellite discovery date is 5 months (17+Y) date ) Day (5 months 17 days are green tide historical average satellite finding date).
C2 green tide main body drifting direction prediction model
According to the drift direction of the green tide body, classifying the green tide body into a Sijin type, a North partial Sixi type, a North upper type and a North partial east type;
constructing a prediction factor of the drift direction of the green tide body: flow direction X ocn_deg_1 And wind direction X wnd_deg_2 The method comprises the steps of carrying out a first treatment on the surface of the Establishing a binary regression model, wherein the binary regression model is represented by the following formula (2);
Y greentide_deg ＝1.03X ocn_deg_1 +0.80X wnd_deg_2 -82.81 (2)
the selected area and time period of each factor are as follows: x is X ocn_deg_1 1,6 months 1 to 3 days; x is X wnd_deg_2 Zone 2,5 months 6 to 6 months 3.
The drift direction angle of the green tide body is Y greentide_deg . For example: 90 ° indicates that the green tide body drifts in the north-north direction, and 135 ° indicates that it drifts in the northwest direction.
c3 green tide maximum distribution area prediction model
Constructing a prediction factor of the maximum distribution area of green tide: sea temperature X sst And the weft component X of the flow ocn_u The method comprises the steps of carrying out a first treatment on the surface of the Establishing a binary regression model, wherein the binary regression model is represented by the following formula (3);
S＝6.36×10 3 X sst +3.96×10 5 X ocn_u -1.33×10 5 (3)
the selected area and time period of each factor are as follows: x is X sst Zone 1,3 months 4 to 4 months 6; x is X ocn_u Zone 1,6 months 2 to 5 days.
The maximum distribution area of the green tide is S.
d, obtaining a meteorological element value and a marine element value of the early stage of the green tide of the required prediction year, and predicting the occurrence and development trend of the green tide of the yellow sea according to the prediction model established in the step c to obtain a prediction result.
In the step a: the meteorological elements comprise air temperature, illumination, precipitation, wind direction and wind speed; the ocean elements include sea temperature, ocean current, wave height and wave direction.
Many researches show that the main factors influencing the growth and drift of green tide comprise meteorological factors such as air temperature, illumination, precipitation, wind direction, wind speed and the like, ocean power factors such as sea temperature, ocean current, wave height, wave direction and the like, and ocean ecological factors such as nutrient salt, pH value and the like. The method is mainly used for establishing a green tide trend prediction model based on meteorological and ocean power factors.
The invention relates to a method for predicting green tide trend, which is divided from time scale, belongs to the category of short-term climate prediction, and mainly analyzes the influence of early signals of atmospheric and ocean heat and power elements on the annual change of green tide without considering the influence of high-frequency signals, so that before a prediction model is established, each atmospheric and ocean element is firstly treated with weather averaging (1 day = 5 days).
In the step b: the atmosphere and ocean data can be The NCEP Climate Forecast System Version (CFS v 2), and the green tide multisource monitoring data is from ocean authorities in China, and the time is 1 month in 2008 to 12 months in 2019.
In the step c1, the establishment of the satellite discovery time prediction model also involves the following:
table 1 lists the first discovery times for satellites in 2008-2019. In 2008-2019, green tide satellite (MODIS) found time from last 5 months to early 6 months, and average and median values were 5 months and 17 days. The earliest is 5 months and 10 days in 2016 and the latest is 6 months and 2 days in 2010. The earliest and the latest differ by 23 days. In general, there is a trend in the first time a satellite finds.
To build a quantitative prediction model, time is quantized to a negative value before the average time and to a positive value after the average time, and the absolute value size indicates the number of days that deviate from the average.
TABLE 1
It is generally considered that the green tide is mainly influenced by atmospheric and ocean thermal factors before being generated, and the green tide is influenced by thermal factors and dynamic factors such as wind, flow and the like after being generated. The method mainly starts from the atmospheric ocean thermal factors in the early stage of green tide generation, analyzes main factors influencing the green tide generation time, and establishes a quantitative prediction model.
Figure 2 is a graph showing the sliding correlation coefficients of a portion of the atmospheric and marine elements with the first time of discovery by the satellite. The correlation of each element with the green tide satellite discovery time was analyzed one by one.
The sea temperature (sst) is from the beginning of the year, the correlation coefficient is continuously negative, the correlation is fluctuated in 2 months, the negative correlation coefficient is stabilized at about-0.4 in the rest time of 1 to 5 months, the lower the sea temperature in the early stage is, the later the green tide finding time is, namely, the higher the sea temperature is, the early generation of the green tide is facilitated.
The sliding correlation coefficient variation trend of the seawater salinity (sal) is similar to the sea temperature, and the sliding correlation coefficient variation trend is in a negative correlation relationship in general, namely, the lower the seawater salinity is, the later the green tide discovery time is. It is understood that higher salinity of seawater may create more favorable conditions for early green tide generation.
The air temperature (at) and the sea temperature are both physical quantities representing the degree of cold and hot, but the amplitude of the change in the correlation coefficient of the air temperature is large because the atmosphere is a high frequency change compared with the sea. As can be seen from fig. 2, the correlation coefficient remained negative from 3 months 4 to 4 months 6 days despite the unstable air temperature signal, indicating that the lower the early air temperature, the later the green tide discovery time.
The sliding correlation coefficient of the precipitation rate (prate) also has strong fluctuation, but the correlation coefficient is generally positive, and 6 correlation coefficients are larger than 0.5, so that the positive correlation relationship between the precipitation and the green tide discovery time is seen, namely, the precipitation is more and the green tide discovery time is later.
The downward short wave radiation (dswsfc) has a weaker correlation than other factors. Wherein, only the negative correlation coefficient of 2 months and 6 days exceeds 0.5, the correlation coefficient of the contrast precipitation rate and the correlation coefficient of the same-period precipitation exceeds 0.8. The two have obvious negative correlation relation through calculation. It follows that the correlation of the down-short wave radiation with the satellite finding time is poor and the predictors are not independent and are therefore not listed as predictors.
In combination with the above, a possible influencing factor of the first discovery time of the satellite is constructed: x is X sst (2 months 5 to 5 months 1), X sal (3 months 6 to 5 months 4), X at (3 months 4 to 4 months 6) and X prate (2 months 2 to 4 months 5).
Table 2 lists the correlation coefficients (R) and standard deviation (STD) for building a one-element/multiple regression model using different combinations of factors. As can be seen from the information listed in table 2, the factor most closely related to satellite finding time (denoted by Y) is precipitation rate, R reaches 0.62, and passes the 95% significance test; sea temperature, salinity and air temperature R were all 0.52, passing the 90% saliency test. The optimal unary regression model is therefore equation (3-1).
Y 1 ＝6.27X prate -11.17 (3-1)
Visible X when building a binary regression model at And X prate Combination, X sst And X prate Compared with a single factor, R is improved, STD is reduced, and the regression model established by matching with air temperature or sea temperature factors has a better prediction effect on the basis of considering precipitation rate. The optimal binary regression model is shown in formula (3-2).
Y 2 ＝-2.44X at +4.95X prate +18.83 (3-2)
If the influence of three factors is considered simultaneously, a ternary regression model is established, and the optimal combination is X sst 、X sal And X prate I.e. a combination of factors of sea temperature, salinity and precipitation, has a better forecasting effect on satellite finding time. The optimal ternary regression model is formula (3-3)
Y 3 ＝-2.90X sst -15.17X sal +3.77X prate +496.32 (3-3)
TABLE 2
FIG. 3 is a graph of the best fit of the unary/binary/ternary regression model versus the first time of discovery by the satellite.
By combining the above, the key factors for predicting the first discovery time of the green tide satellite are sea temperature (sst, 2 months 5 to 5 months 1), salinity (sal, 3 months 6 to 5 months 4) and precipitation rate (prate, 2 months 2 to 4 months 5), the optimal prediction model is formula (3-3), the correlation coefficient of the predicted value and the observed value is 0.73, and the model error is 5.64d.
In the step c2, the green tide body drift direction prediction model is established, and the following matters are also involved:
after green tide is generated, the green tide drifts from the south to the north in the yellow sea area. Because of the annual difference between the atmospheric and marine environmental elements, the green tide drift paths of each year are also different, and especially the things of the drift paths are different, the landing position and disaster-causing area of the green tide are directly affected.
The green tide body is classified into a west TYPE (including a backward steering after west) (TYPE-W), a north-west TYPE (TYPE-NW), a north-superior TYPE (TYPE-N) and a north-east TYPE (TYPE-NE) according to a drift direction of the green tide body, see fig. 4.
Fig. 5 to 7 synthesize wind fields and flow fields in the early stage of green tide generation and the early stage of development in the drift direction. The method sequentially comprises the following steps from left to right: west, north-upper, north-east; and (3) the following steps: a wind farm; the following steps: a flow field. In 5 months 4-6, the main wind directions in (b), (c) and (d) in fig. 5 have clockwise change trend, which respectively correspond to north-west type, north-upper type and north-east type paths, in fig. 5 (a) is characterized in that southeast wind is from south to north on the west side of the yellow sea, and the green tide path is west-forward type due to continuous southeast wind component. In this period, the flow direction difference corresponding to each path is not obvious, but there is a tendency that the flow rate gradually decreases from (e) to (h) in fig. 5 as seen from the flow rate.
The wind and flow differences corresponding to different paths are more obvious in the period of 1-3 months, and the wind and flow directions are shown, and the wind speed and the flow speed and the spatial distribution of the large-value area of the wind speed and the flow speed are also shown. From the wind field, the large wind speed area corresponding to the West-in path is positioned in the southwest of the yellow sea, coastal in Jiangsu province, and the main wind direction is ESE; the wind speed large value area range of the north-partial west type path is enlarged compared with the west-forward type path, the north-partial west type path expands to the east, and the main body wind direction rotates clockwise and is approximately in the SE direction; the wind speed large-value area of the north-top type path is further increased, almost the whole southeast sea area of the yellow sea is covered, and the main wind direction further rotates clockwise and is approximately SSE; the wind field corresponding to the north-east deviation path has larger difference with other wind fields, and the wind speed of the south sea area in the yellow sea is smaller as a whole, which indicates that the drifting speed of the north-east deviation green tide in the early development stage is smaller.
It should be noted that although the direction of the wind vector rotates clockwise, the direction of the wind vector does not completely coincide with the direction of the actual green tide drift, and the green tide drift direction is far to the right than the wind direction, which means that the effect of the flow cannot be ignored. The sea area wind and the current qualitatively meet the relation of the Aickmann drift, and further the effect of the current is proved. In fig. 6, (e) - (h) are flow field distributions corresponding to different paths, and since the difference of the flow vectors in the graph is not easily resolved, the correlation between the flow and the green tide drift direction will be quantitatively calculated.
And 4-6 months, wherein wind fields corresponding to the north-west type and the north-upper type are weakened, and wind fields corresponding to the west-east type and the north-east type are strengthened. In particular, the north-east wind field is obviously enhanced, 35 degrees N has south to southwest wind in the north sea area, and the east flow of the corresponding flow field is also enhanced, which indicates that the green tide of the north-east path increases in the north-east moving speed in the period. The above is a qualitative analysis, and the influence of each factor on the drift direction is quantitatively analyzed below.
And (3) respectively dividing the wind and the flow into areas to average, and calculating the correlation coefficient of the wind and the flow change from time to time and the drift direction. For the convenience of calculation, the green tide drift direction, wind direction and flow direction angles are unified on a plane rectangular coordinate system (for example, 90 degrees indicate that the green tide drifts in the northwest direction, north direction flows, south wind, 135 degrees indicate that the green tide drifts in the northwest direction, northwest direction flows, southeast wind and the like), and the influence of the flow field of the area 1 and the wind field of the area 2 on the green tide drift direction is obvious (the correlation of the wind field of the area 1 and the flow field of the area 2 does not pass the 95% confidence test). The result shows that in the south sea area of 35 degrees N, the influence of the flow field on the green tide drift direction is more obvious; and in the North sea area at 35 degrees N, the influence of the wind field on the green tide drift direction is more obvious.
The prior research results indicate that the green tide algae mainly grow below the sea surface in the early stage of generation, and gradually develop and mature in the north floating process of the green tide algae, and float to the sea surface. The research of the invention shows that the green tide algae in the south sea area at 35 degrees N is mainly driven by flow, and the green tide algae in the North sea area at 35 degrees N is mainly driven by wind, which further proves the prior research results.
FIG. 8 shows that the flow direction and the drift direction are generally in positive correlation, indicating that the flow direction angle increases and the drift direction angle increases; the flow rate is in a negative correlation with the drift direction in the period of 4-6 months, which shows that the smaller the flow rate is, the larger the drift angle (western). FIG. 9 shows that the wind direction and the drift direction are also positively correlated, and correlation coefficients of 5 months 6, 6 months 2 and 6 months 3 pass the significance test; and the correlation coefficient of wind speed only passes the significance test in 6 months 1.
And defining a prediction factor of the green tide drift direction according to the correlation coefficient curve. In defining the factor, the following principles are considered: (1) the selected factors have a certain continuity; (2) The time for defining the factors is early or late, so that the timeliness of prediction is guaranteed; (3) the influence of different areas and different driving forces is considered. Two predictors are thus defined: (1) 6 months 1-3 days the flow direction of zone 1 (ocn_deg_1); (2) wind direction (wnd_deg_2) of 5 month 6-6 month 3 zone 2.
And (3) adopting the same method as in the step c1, and establishing a unitary/binary optimal regression model of the green tide drift direction.
The optimal unary regression model was equation (3-6), the fit correlation coefficient was 0.73, and the STD was 14.97 ° by 95% saliency test.
Y greentide_deg ＝0.98X wnd_deg_2 +0.82 (3-6)
The optimal binary regression model was equation (3-7), the fitted correlation coefficient was 0.84, and the STD was 12.32 ° by 95% saliency test.
Y greentide_deg ＝1.03X ocn_deg_1 +0.80X wnd_deg_2 -82.81 (3-7)
Table 3 shows the results of the tests for different factor combinations to build the unary/multiple regression model.
TABLE 3 Table 3
Fig. 10 shows a graph of optimal unary/binary regression model fit versus green tide drift direction.
In combination with the above, the key factors for predicting the green tide drift direction are the flow direction (ocn_deg, 6 months 1-3 days) of the region 1 and the wind direction (wnd_deg, 5 months 6 days-6 months 3 days) of the region 2, the optimal prediction model is the formula (3.7), the correlation coefficient of the predicted value and the observed value is 0.84, and the model error is 12.32 °.
In the step c3, the establishment of the maximum distribution area of green tide is also related to the following:
in the occurrence and development of green tide, not only are the routes different in each year, but also the development scale is internationally changed.
There are two factors characterizing green tide scale, namely distribution area and coverage area. The green tide distribution area refers to the total area within the envelope of the whole sea area where the floating green tide is found; the green tide coverage area refers to the area of the sea area actually covered by the green tide.
Fig. 11 shows the annual change in the maximum distribution area and the maximum coverage area throughout the year 2008 to 2019. The maximum distribution area reaches or exceeds 50000km 2 In 5 years, 2009, 2014, 2015, 2016 and 2019 respectively, wherein the distribution area in 2009 was the largest, reaching 58000km 2 The method comprises the steps of carrying out a first treatment on the surface of the The distribution area in 2012 is minimum and less than 20000km 2 The method comprises the steps of carrying out a first treatment on the surface of the Average distribution area about 40000km 2 . Coverage area maxima also occur in 2009, approximately 2100km 2 The rest years are not more than 1000km 2 . Most preferably, the first to fourth193km with small value 2018 2 . Average coverage area of about 620km 2 。
Green tide is accompanied by a north drift during growth, and the scale of green tide development may be affected by sea-land distribution and drift paths. The above mentioned maximum distribution area reaches or exceeds 50000km 2 In 2009, 2014, 2015, 2016, and 2019, all of which were the green tide body drift path was in the east or the drift direction was in the north. In 2009, the generation position and the drift path of the green tide are all eastern, which indicates that the position and the path of the green tide have certain correlation with the maximum distribution area.
If the green tide generation time is mainly related to the sea gas thermal power factor, the green tide drift path is mainly related to the sea gas power, and the maximum distribution area of the green tide is the result of the combined action of the sea gas thermal power factor and the power factor.
In order to seek the influence factors of the maximum distribution area of green tide, sea gas thermal power factors and sea gas dynamic factors which are possibly related to the development scale of the green tide are selected, and the correlation coefficients of the average value of each element area of the area 1, the area 2 and the area 1+2 and the maximum distribution area of the current year are calculated according to the area division.
TABLE 4 Table 4
Table 4 lists possible factors affecting the green tide scale of development, wherein the correlation coefficients of 6 variables, which pass the significance test, are sea temperature, salinity, precipitation rate, surface short wave radiation, latitudinal component of wind and latitudinal component of flow, respectively, and as shown in fig. 12, the numbers after the variable names represent the areas, and the boxes mark the periods of stronger correlation.
In fig. 12, the critical influence areas of 5 factors are all located in zone 1, and the critical influence area of only salinity is zone 2, indicating that the atmosphere and the marine environment in the early and early stages of green tide generation dominate the development scale of green tide. The correlation of each factor is specifically analyzed as follows.
Of all factors, the strongest correlation and the longest signal duration is sst. The continuous positive correlation signal indicates that the higher the early sea temperature, the larger the green tide distribution area. The correlation between sst and green tide distribution area changes from positive to negative before and after 7 months 1, which indicates that after the sst rises and reaches a critical point of temperature in 7 months, the growth of the sst on green tide algae changes from promotion effect to inhibition effect.
Unlike other factors, sal is the only factor affecting critical area, region 2, whose critical impact period is at the beginning of 7 months, indicating that when green tide progresses to its maximum, how much salt in the sea water has a greater impact on whether green tide can continue to progress.
Precipitation rate and downward short wave radiation are a set of physical quantities related to the water vapor content in the atmosphere. prate and green tide maximum distribution area are in positive correlation in 5 months 3, but the signal duration is shorter, and the generation effect of green tide is to be examined; and the prate and the maximum distribution area of the green tide are in negative correlation before and after 7 months 2, which indicates that the rainfall in the full-scale green tide development period can cause the reduction of the green tide area. The key influence time of downward short wave radiation is about 6 months and about 2 days, which shows that in the early stage of green tide generation, sufficient illumination is beneficial to the growth of green tide.
The latitudinal components of wind and flow belong to the dynamic factors that affect the maximum green tide distribution area by affecting the green tide's drift path. Specifically, when the latitudinal components of the wind and flow are positive, the green tide drifting to the east is facilitated, and the eastern open sea area is beneficial to the development and diffusion of the green tide.
When selecting the influencing factors of the maximum distribution area of green tide, the following principles need to be considered: (1) The factor strong correlation signal needs to have a certain time duration; (2) The time of defining the factors is early and late, so that the timeliness of green tide prediction is guaranteed; (3) The influence of the atmosphere and the ocean, and the influence of the heat and the power factors are considered. Thus defining 3 predictors: x is X sst (3 months 4 to 4 months 6), X dswsfc (6 months 2-3 days), X ocn_u (6 months 2 to 5).
Table 5 lists the correlation coefficients (R) and standard deviation (STD) for building a one-element/multiple regression model using different combinations of factors. As can be seen from the information listed in the table, the factor best related to the maximum distribution area (S) of green tide is X ocn_u R reaches 0.75, passing the 95% saliency test. The optimal unary regression model is therefore:
S 1 ＝5.45×10 5 X ocn_u +5.68×10 4 (3-8)
at X ocn_u Increasing X on the basis of factors sst Factor, R increases to 0.80 and STD decreases to 9.58×10 3 The optimal binary regression model is:
S 2 ＝6.36×10 3 X sst +3.96×10 5 X ocn_u -1.33×10 5 (3-9)
if the influence of three factors is considered at the same time, a ternary regression model is built, and the obtained prediction effect is not improved compared with that of the binary regression model. Therefore, the formula (3-9) is adopted as a prediction model of the maximum distribution area of green tide.
TABLE 5
FIG. 13 is a graph of the best fit of the unary/binary regression model versus the maximum green tide distribution area.
By combining the above, the key factors for predicting the maximum distribution area of green tide are sea temperature (sst, 3 months 4 to 4 months 6) of the region 1 and weft component (ocn_u, 6 months 2-5) of the flow, the optimal prediction model is formula (3.9), the correlation coefficient of the predicted value and the observed value is 0.80, and the model error is 9.58×10 3 km 2 。
The invention is further described in connection with specific examples of applications in 2020 and 2021:
(1) Application and evaluation of green tide prediction in 2020
By applying the method, the ocean and atmosphere observation data in 2020 are substituted into the model, the first satellite discovery time of green tide is calculated to be 5 months and 17 days, the main body drift direction is 18 degrees in north, and the maximum distribution area is 46521 square kilometers.
According to the ocean disaster bulletin of the North sea area in 2020 (hereinafter referred to as the "2020 bulletin") and satellite remote sensing monitoring data, the yellow sea coastal area of China is affected by green tide disasters from the last ten days of 4 months to the last ten days of 7 months in 2020. For 21 days 5 months, satellite remote sensing finds green tide of enteromorpha in the sea area about 20km in the north of the North-Subei shoal culture area for the first time, the distribution area is 1654 square km, and the coverage area is 5 square km; after 11 days of 6 months, green tide of enteromorpha sequentially affects offshore areas such as Qingdao, smoke table, sunshine, wired sea and the like; for 23 days of 6 months, the distribution area of green tide of the enteromorpha reaches the maximum, namely 18237 square kilometers; in the late 7 months, green tide of Enteromorpha is basically eliminated. The green tide body drift direction is approximately 30 ° north-west.
And (3) checking the predicted result, wherein the predicted time of the first satellite finding time is 5 months and 17 days, the actual occurrence time is 5 months and 21 days, the error is 4 days, and the error is smaller than the model error (5.64 days). The predicted value of the drift direction of the green tide body is 18 degrees in north, and is actually 30 degrees in north, the direction is accurate, the angle deviation is 12 degrees, and the angle deviation is smaller than the model error (12.32 degrees). The predicted value of the maximum distribution area of the green tide is 46521 square kilometers, the actual value is about 18237 square kilometers, and the deviation exceeds the allowable error of the model. According to the record of ' 2020 publication ', the ' natural resource department in 2020 and the common organization of the purple seaweed cultivation area in North Subei radiation of Jiangsu province develop an enteromorpha green tide prevention and control test. The initial biomass of green algae of the enteromorpha is controlled from the source by carrying out algae removal operation, recovering the laver culture raft frame in advance and the like. Compared with the average value of the last five years, the maximum coverage area of the enteromorpha green tide in 2020 is reduced by 54.9%, and the duration is shortened by about 30 days. The green tide prevention and control test of enteromorpha achieves remarkable effect. The actual distribution area of green tide is far smaller than the predicted area, and is mainly caused by a green tide prevention and control experiment. The establishment of the prediction model aims at natural influence factors such as ocean and meteorological factors, and does not contain human intervention.
(2) Application and evaluation of 2021 green tide prediction
By applying the method, the ocean and atmosphere observation data in 2020 are substituted into the model, the first satellite discovery time of green tide is calculated to be 5 months and 14 days, the main body drift direction is 6 degrees in north, and the maximum distribution area is 57633 square kilometers.
According to the remote sensing monitoring data of the North sea bureau satellite of the natural resource department, the satellite first discovers a regular floating green tide in the sea area near the North Subei shoal on the 5 th month 17 th year 2021; then gradually drifting to north, and affecting the offshore areas of Qingdao city, japanese market, tobacco stand city and Weihai city successively; the distribution area of yellow sea enteromorpha green tide reaches 61898 square kilometers on 21 days of 6 months, and is the maximum value in the current year and the maximum value observed from the occurrence of green tide disasters; the green tide starts in 8 months and enters the extinction period until the next ten days basically die. The green tide body drift direction is approximately 2 ° north-west.
And (3) checking the predicted result, wherein the predicted time of the first satellite finding time is 5 months and 14 days, the actual occurrence time is 5 months and 17 days, and the error is 3 days and is smaller than the model error (5.64 days). The predicted value of the drift direction of the green tide body is north-west 6 degrees, the actual north-west 2 degrees, the direction is accurate, the angle deviation is 4 degrees, and the angle deviation is smaller than the model error (12.32 degrees). The predicted value of the maximum distribution area of the green tide is 57633 square kilometers, the actual value is about 61898 square kilometers, the error is 4265 square kilometers, and the predicted value is smaller than the model error (9580 square kilometers).
Through the green tide prediction application and evaluation in 2020 and 2021, except that the green tide scale in 2020 is affected by artificial treatment, the predicted value of each element is smaller than the predicted error, and the model application effect is good.
The parts not described in the above modes can be realized by adopting or referring to the prior art.
It should be noted that, under the teaching of the present specification, any equivalent or obvious modification made by those skilled in the art should fall within the scope of the present invention.
Claims (4)
1. The method for predicting the medium-long-term trend of the yellow sea green tide is characterized by comprising the following steps of:
a, dividing a yellow sea area into two key areas for green tide generation and development by using 35 DEG N as a boundary, namely an area 1 and an area 2, wherein the area 1 ranges from 33 DEG N to 35 DEG N,119 DEG E to 123 DEG E, the area 2 ranges from 35 DEG N to 37 DEG N,119 DEG E to 123 DEG E, and main factors affecting the green tide growth and drift are determined to be weather factors and ocean factors;
b, acquiring green tide satellite remote sensing, unmanned aerial vehicle, ship multi-source historical monitoring data and meteorological and marine element historical analysis data sets of key areas;
c, analyzing early weather and ocean influence factors of three indexes of green tide satellite discovery time, main body drift direction and maximum distribution area based on the acquired data, and establishing a prediction model;
c1 satellite finding time prediction model
Constructing a satellite first-time discovery time prediction factor: sea temperature X sst Salinity X of sea water sal And precipitation rate X prate The method comprises the steps of carrying out a first treatment on the surface of the Establishing a ternary regression model, wherein the ternary regression model is shown as the following formula (1);
Y date ＝-2.90X sst -15.17X sal +3.77X prate +496.32 (1)
the selected area and time period of each factor are as follows: x is X sst Zone 1,2 months 5 to 5 months 1, 1 = 5 days; x is X sal Zone 1,3 months 6 to 5 months 4; x is X prate Zone 1,2 months 2 to 4 months 5;
the actual generation time of green tide is 5 months 17+Y date Day of the year;
c2 green tide main body drifting direction prediction model
According to the drift direction of the green tide body, classifying the green tide body into a Sijin type, a North partial Sixi type, a North upper type and a North partial east type;
constructing a prediction factor of the drift direction of the green tide body: flow direction X ocn_deg_1 And wind direction X wnd_deg_2 The method comprises the steps of carrying out a first treatment on the surface of the Establishing a binary regression model, wherein the binary regression model is represented by the following formula (2);
Y greentide_deg ＝ 1.03X ocn_deg_1 +0.80X wnd_deg_2 -82.81 (2)
the selected area and time period of each factor are as follows: x is X ocn_deg_1 1,6 months 1 to 3 days; x is X wnd_deg_2 2,5 months 6 to 6 months 3;
the drift direction angle of the green tide body is Y greentide_deg The method comprises the steps of carrying out a first treatment on the surface of the 90 degrees indicates that the green tide body drifts in the north-north direction, and 135 degrees indicates that the green tide body drifts in the northwest direction;
c3 green tide maximum distribution area prediction model
Constructing a prediction factor of the maximum distribution area of green tide: sea temperature X sst And the weft component X of the flow ocn_u The method comprises the steps of carrying out a first treatment on the surface of the Establishing a binary regression model, wherein the binary regression model is represented by the following formula (3);
S＝6.36×10 3 X sst +3.96×10 5 X ocn_u -1.33×10 5 (3)
the selected area and time period of each factor are as follows: x is X sst Zone 1,3 months 4 to 4 months 6; x is X ocn_u Zone 1,6 months 2 to 5 days;
the maximum distribution area of the green tide is S;
d, obtaining a meteorological element value and a marine element value of the early stage of the green tide of the required prediction year, and predicting the occurrence and development trend of the green tide of the yellow sea according to the prediction model established in the step c to obtain a prediction result.
2. The method for predicting medium-long term trend in yellow sea green tide according to claim 1, wherein in step b: the meteorological elements comprise air temperature, illumination, precipitation, wind direction and wind speed; the ocean elements include sea temperature, ocean current, wave height and wave direction.
3. The method for predicting medium-long term trend in yellow sea green tide according to claim 1, wherein in step c: and establishing a prediction model by adopting a synthetic analysis, a lead-lag correlation analysis and a multiple regression analysis method through meteorological element data, ocean element data and green tide monitoring data corresponding to the current year.
4. A method for predicting medium-long term trend in yellow sea green tide according to claim 3, wherein: before the prediction model is established, weather averaging and area averaging are carried out on each meteorological element data and ocean element data.
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